Improving music recommendation by incorporating social influence

In the past decades, a large number of music pieces are uploaded to the Internet every day through social networks, such as Last.fm, Spotify and YouTube, that concentrates on music and videos. We have been witnessing an ever-increasing amount of music data. At the same time, with the huge amount of online music data, users are facing an everyday struggle to obtain their interested music pieces. To solve this problem, music search and recommendation systems are helpful for users to find their favorite content from a huge repository of music. However, social influence, which contains rich information about similar interests between users and users’ frequent correlation actions, has been largely ignored in previous music recommender systems. In this work, we explore the effects of social influence on developing effective music recommender systems and focus on the problem of social influence aware music recommendation, which aims at recommending a list of music tracks for a target user. To exploit social influence in social influence aware music recommendation, we first construct a heterogeneous social network, propose a novel meta path-based similarity measure called WPC, and denote the framework of similarity measure in this network. As a step further, we use the topological potential approach to mine social influence in heterogeneous networks. Finally, in order to improve music recommendation by incorporating social influence, we present a factor graphic model based on social influence. Our experimental results on one real world dataset verify that our proposed approach outperforms current state-of-the-art music recommendation methods substantially.

     

Typicality‐based collaborative filtering for book recommendation

Nowadays, personalized recommender system placed an important role to predict the customer needs, interest about particular product in various application domains, which is identified according to the product ratings. During this process, collaborative filtering (CF) has been utilized because it is one of familiar techniques in recommender systems. The conventional CF methods analyse historical interactions of user‐item pairs based on known ratings and then use these interactions to produce recommendations. The major challenge in CF is that it needs to calculate the similarity of each pair of users or items by observing the ratings of users on same item, whereas the typicality‐based CF determines the neighbours from user groups based on their typicality degree. Typicality‐based CF can predict the ratings of users with improved accuracy. However, to eliminate the cold start problem in the proposed recommender system, the demographic filtering method has been employed in addition to the typicality‐based CF. A weighted average scheme has been applied on the combined recommendation results of both typicality‐based CF and demographic‐based CF to produce the best recommendation result for the user. Thereby, the proposed system has been able to achieve a coverage ratio of more than 95%, which indicates that the system is able to provide better recommendation for the user from the available lot of products.     

Entertainment recommender systems for group of users

Recommender systems are used to recommend potentially interesting items to users in different domains. Nowadays, there is a wide range of domains in which there is a need to offer recommendations to group of users instead of individual users. As a consequence, there is also a need to address the preferences of individual members of a group of users so as to provide suggestions for groups as a whole. Group recommender systems present a whole set of new challenges within the field of recommender systems. In this article, we present two expert recommender systems that suggest entertainment to groups of users. These systems, jMusicGroupRecommender and jMoviesGroupRecommender, suggest music and movies and utilize different methods for the generation of group recommendations: merging recommendations made for individuals, aggregation of individuals’ ratings, and construction of group preference models. We also describe the results obtained when comparing different group recommendation techniques in both domains.     

A novel social network hybrid recommender system based on hypergraph topologic structure

With the advent and popularity of social network, more and more people like to share their experience in social network. However, network information is growing exponentially which leads to information overload. Recommender system is an effective way to solve this problem. The current research on recommender systems is mainly focused on research models and algorithms in social networks, and the social networks structure of recommender systems has not been analyzed thoroughly and the so-called cold start problem has not been resolved effectively. We in this paper propose a novel hybrid recommender system called Hybrid Matrix Factorization(HMF) model which uses hypergraph topology to describe and analyze the interior relation of social network in the system. More factors including contextual information, user feature, item feature and similarity of users ratings are all taken into account based on matrix factorization method. Extensive experimental evaluation on publicly available datasets demonstrate that the proposed hybrid recommender system outperforms the existing recommender systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approaches.     

Privileged contextual information for context-aware recommender systems

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system.     

社交媒体中的信息推荐

近年来社交媒体越来越流行,可以从中获得大量丰富多彩的信息的同时,也带来了严重的"信息过载"问题.推荐系统作为缓解信息过载最有效的方法之一,在社交媒体中的作用日趋重要.区别于传统的推荐方法,社交媒体中包含大量的用户产生内容,因此在社交媒体中,通过结合传统的个性化的推荐方法,集成各类新的数据、元数据和清晰的用户关系,产生了各种新的推荐技术.总结了社交推荐系统中的几个关键研究领域,包括基于社会化标注的推荐、组推荐和基于信任的推荐,之后介绍了在信息推荐中考虑时间因素时的情况,最后对社交媒体中信息推荐有待深入研究的难点和发展趋势进行了展望.     

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.     

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.     

一种融合项目特征和移动用户信任关系的推荐算法

协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度;而在稀疏的评分数据中,用户间共同评分的项目所占比重较小,不能准确地找到偏好相似的用户,从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算,使用推土机距离（earth mover's distance,简称EMD）实现跨项目的移动用户相似度计算,提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比,融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度.     

一种解决新项目冷启动问题的推荐算法

推荐系统作为缓解信息过载问题的有效方法之一,在社交媒体中的作用日趋重要.但是,新项目冷启动和新用户冷启动问题是推荐技术面临的难题.为了解决新项目冷启动问题,提出了用户时间权重信息概念,该定义考虑到了用户评价时间与项目发布时间的时间间隔,根据用户时间权重值的大小,可以判断该用户是积极用户还是消极用户,以及用户对新项目的偏爱程度;利用三分图的形式来描述用户-项目-标签、用户-项目-属性之间的关系.在充分考虑用户、标签、项目属性、时间等信息基础上,获得个性化的预测评分值公式,提出了推荐算法.实验结果表明:所提出的方法能够实现满足不同用户、不同偏好的个性化推荐,在为用户推荐到合适项目的同时还能带来惊喜.比较实验说明,所提出的方法推荐准确度高,推荐新颖度高.交叉验证实验结果表明:该方法在解决推荐算法中的新项目冷启动问题上,无论是在推荐的准确度还是推荐项目的新颖度上都是有效的.     

一种改进的偏好融合组推荐方法

近年来,组推荐系统已经逐渐成为推荐系统领域的研究热点之一.在电影电视和旅游推荐中,用户常常是参与活动的一组人,这就需要为多个用户形成的群组进行推荐.作为解决群组推荐问题的有效手段,组推荐系统将单个用户推荐扩展为群组推荐,目前已经应用在新闻、音乐、电影、餐饮等诸多领域.现有的组推荐融合方法主要是模型融合与推荐融合,其效用好坏目前仍没有定论,并且它们各有自己的优缺点.模型融合存在着群组成员间的公平性问题,推荐融合忽视了群组成员间的交互.提出一种改进的偏好融合组推荐方法,它结合了两种融合方法的优点.同时根据实验得出了"群组偏好与个人偏好具有相似性"的结论,并将它结合在改进方法中.最后,通过在Movielens数据集上的实验分析,验证了该方法的有效性,证明了它能够有效地提高推荐准确率.     

A novel mobile recommender system for indoor shopping

With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users’ preferences by analyzing users’ positions, without requiring users’ explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances.     

A Multicriteria Collaborative Filtering Approach Using the Indifference Relation and its Application to Initiator Recommendation for Group-Buying

For recommender systems, the main aim of the popular collaborative filtering approaches is to recommend items that users with similar preferences have liked in the past. Single-criterion recommender systems have been successfully used in several applications. Because leveraging multicriteria information can potentially improve recommendation accuracy, multicriteria rating systems that allow users to assign ratings to various content attributes of items they have consumed have become the focus in recommendation systems. By treating the recommendation of items as a multicriteria decision problem, it is interesting to incorporate the preference relation of users of multicriteria decision making (MCDM) into the similarity measure for a collaborative filtering approach. For this, the well-known indifference relation can justify a discrimination or similarity between any two users, if outranking relation theory is incorporated. The applicability of the proposed single-criterion and multicriteria recommendation approaches to the recommendation of initiators on a group-buying website was examined. Experimental results have demonstrated that the generalization ability of the proposed multicriteria recommendation approach performs well in comparison to other single-criterion and multicriteria collaborative filtering approaches.     

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.     

Personalized recommender system based on friendship strength in social network services

The rapid growth of social network services has produced a considerable amount of data, called big social data. Big social data are helpful for improving personalized recommender systems because these enormous data have various characteristics. Therefore, many personalized recommender systems based on big social data have been proposed, in particular models that use people relationship information. However, most existing studies have provided recommendations on special purpose and single-domain SNS that have a set of users with similar tastes, such as MovieLens and Last.fm; nonetheless, they have considered closeness relation. In this paper, we introduce an appropriate measure to calculate the closeness between users in a social circle, namely, the friendship strength. Further, we propose a friendship strength-based personalized recommender system that recommends topics or interests users might have in order to analyze big social data, using Twitter in particular. The proposed measure provides precise recommendations in multi-domain environments that have various topics. We evaluated the proposed system using one month's Twitter data based on various evaluation metrics. Our experimental results show that our personalized recommender system outperforms the baseline systems, and friendship strength is of great importance in personalized recommendation.     

Sequence-based trust in collaborative filtering for document recommendation

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors – time factor and document similarity – in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.     

Progress in context-aware recommender systems — An overview

Recommender Systems are the set of tools and techniques to provide useful recommendations and suggestions to the users to help them in the decision-making process for choosing the right products or services. The recommender systems tailored to leverage contextual information (such as location, time, companion or such) in the recommendation process are called context-aware recommender systems. This paper presents a review on the continual development of context-aware recommender systems by analyzing different kinds of contexts without limiting to any specific application domain. First, an in-depth analysis is conducted on different recommendation algorithms used in context-aware recommender systems. Then this information is used to find out that how these techniques deals with the curse of dimensionality, which is an inherent issue in such systems. Since contexts are primarily based on users’ activity patterns that leads to the development of personalized recommendation services for the users. Thus, this paper also presents a review on how this contextual information is represented (either explicitly or implicitly) in the recommendation process. We also presented a list of datasets and evaluation metrics used in the setting of CARS. We tried to highlight that how algorithmic approaches used in CARS differ from those of conventional RS. In that, we presented what modification or additions are being applied on the top of conventional recommendation approaches to produce context-aware recommendations. Finally, the outstanding challenges and research opportunities are presented in front of the research community for analysis     

旅游推荐系统研究综述

为用户提供个性化推荐服务并提高推荐的准确度和用户满意度,是当前旅游推荐系统的主要研究任务。文中分析了旅游推荐系统与传统推荐系统的异同点,并从基于内容的推荐、基于协同过滤的推荐、基于知识的推荐、基于人口统计的推荐、混和型推荐以及基于位置感知的推荐共6个方面考查了旅游推荐的研究现状。在此基础上,给出了旅游推荐系统的一个总体框架。最后,总结分析了旅游推荐系统面临的6个重点和难点问题,并指出了下一步需要关注的研究方向。     

Detection of the customer time-variant pattern for improving recommender systems

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems—content-based recommending and collaborative filtering. So far, collaborative filtering recommender systems have been very successful in both information filtering and e-commerce domains. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest.This paper suggests a methodology for detecting a user's time-variant pattern in order to improve the performance of collaborative filtering recommendations. The methodology consists of three phases of profiling, detecting changes, and recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes.